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Infinite-dimensional multilayer perceptrons

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2 Author(s)
Kuzuoglu, Mustafa ; Dept. of Electr. & Electron. Eng., Middle East Tech. Univ., Ankara, Turkey ; Leblebicioglu, Kemal

In this paper a new multilayer perceptron (MLP) structure is introduced to simulate nonlinear transformations on infinite-dimensional function spaces. This extension is achieved by replacing discrete neurons by a continuum of neurons, summations by integrations and weight matrices by kernels of integral transforms. Variational techniques have been employed for the analysis and training of the infinite-dimensional MLP (IDMLP). The training problem of IDMLP is solved by the Lagrange multiplier technique yielding the coupled state and adjoint state integro-difference equations. A steepest descent-like algorithm is used to construct the required kernel and threshold functions. Finally, some results are presented to show the performance of the new IDMLP

Published in:

Neural Networks, IEEE Transactions on  (Volume:7 ,  Issue: 4 )